{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T21:13:30Z","timestamp":1777670010851,"version":"3.51.4"},"reference-count":149,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Artificial intelligence is revolutionizing healthcare. In the recent years, AI tools have been incorporated by medical specialties that heavily rely on imaging techniques to aid in the diagnosis, management, and monitoring of a wide array of clinical conditions. Methods: Thoracic surgery is not an exception: AI is becoming a reality, although it is only the beginning. AI-based tools can be employed in medicine, and by extracting useful information from big data, they allow for the early diagnosis of diseases like lung cancer. Diagnostic imaging is the most promising clinical application of AI in medicine. Results: As for other specialties, ethical issues represent a challenge in thoracic surgery and must be addressed before introducing these applications. Data protection and biases, privacy, \u2018the black box\u2019 problem (explainability), and responsibility are some challenges that AI must supplant. Conclusions: In this review, the authors aim to highlight the importance of AI in thoracic surgery. AI applications, future directions, and clinical benefits and challenges, particularly in this area, will be addressed, highlighting solutions to successfully incorporate AI into healthcare protocols.<\/jats:p>","DOI":"10.3390\/diagnostics15141734","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T09:12:48Z","timestamp":1751965968000},"page":"1734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence in Thoracic Surgery: Transforming Diagnostics, Treatment, and Patient Outcomes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9166-9508","authenticated-orcid":false,"given":"Sara","family":"Lopes","sequence":"first","affiliation":[{"name":"Portuguese Institute of Oncology of Porto, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-0830","authenticated-orcid":false,"given":"Miguel","family":"Mascarenhas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal"},{"name":"Precision Medicine Unit, Department of Gastroenterology, Hospital S\u00e3o Jo\u00e3o, 4200-437 Porto, Portugal"},{"name":"WGO Training Center, 4200-437 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0887-8796","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal"},{"name":"Precision Medicine Unit, Department of Gastroenterology, Hospital S\u00e3o Jo\u00e3o, 4200-437 Porto, Portugal"},{"name":"WGO Training Center, 4200-437 Porto, Portugal"}]},{"given":"Maria Gabriela O.","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal"},{"name":"Institute for Research and Innovation in Health\u2014Associate Laboratory (i3s-LA) (IPATIMUP\/i3s), 4200-135 Porto, Portugal"}]},{"given":"Adelino F.","family":"Leite-Moreira","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal"},{"name":"Department of Cardiothoracic Surgery, Hospital S\u00e3o Jo\u00e3o, 4200-437 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"ref_1","unstructured":"(2025, March 15). 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